Design Intelligent Systems using LLMs, RAG, and Agentic AI

AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact

Application closes on 14th May 2026

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Top rated online AI, Data Science, and ML course

Application closes 14th May 2026

Distinctive features

AI-assisted coding

Leverage AI-assisted coding tools to write and debug Python faster, including access to OpenAI APIs and Codex for hands-on practice at no additional cost.
Next-gen modules

The upgraded curriculum is now infused with GPT-5, Codex for advanced code generation, LangChain, and LangGraph for building modern AI workflows and agentic systems.

Program Outcomes

Elevate your career in AI, Data Science and ML

Build proficiency in advanced topics like Agentic AI, LLM Orchestration, and RAG

Explain how AI evolved from prediction models to language models and autonomous agents
Write effective prompts, detect hallucinations, and use AI coding assistants to write and debug Python
Given a business question, choose the right ML approach, apply it, and assess if results are trustworthy
Connect LLMs to external data using RAG to ground outputs in real data and assess pipeline performance
Build AI systems that plan a sequence of steps, use external tools, and complete tasks autonomously
Design pipelines where multiple AI agents collaborate, divide work, recover from errors, and boost performance

Earn a certificate of completion from MIT IDSS

#1 in World Universities

QS World University Rankings, 2025
#1 in AI and Data Science

QS World University Rankings by Subject, 2025
#2 in National Universities

U.S. News & World Report Rankings, 2024-2025

Key program highlights

Why choose the AI and Data Science program

Learn from MIT faculty

Learn from the vast knowledge of MIT AI, Data Science and Machine Learning faculty through recorded sessions.
AI-assisted coding

Build AI and data science skills using AI-assisted coding tools like GitHub Copilot and ChatGPT to write, debug, and learn Python through hands-on practice.
Advanced AI-infused curriculum

Explore advanced modules on emerging topics including prompt engineering, retrieval-augmented generation (RAG), and next-generation model architectures.
Personalized mentorship sessions

Benefit from personalized weekend mentorship by experienced AI, Data Science and ML practitioners from leading global organizations.
Dedicated program support

Connect with dedicated program managers to assist with queries and guide you throughout the course.
Build Real-World Expertise

Work on 4 hands-on projects and explore 10+ real-world case studies to strengthen your practical skills and demonstrate your AI and Data Science capabilities.

Skills you will learn

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

This program is ideal for

Professionals ready to advance their skills in AI, Data Science, and Machine Learning

View Batch Profile

Career Starters in AI and Data Science

Individuals seeking a structured foundation in AI and Data Science to build job-ready technical capabilities and a strong professional credential.
Early-Career Professionals in Data and Technology

With a foundation in data science or software development, seeking to deepen technical expertise and design end-to-end AI workflows.
Tech Innovators and AI Practitioners

Responsible for building, integrating, or scaling AI solutions, seeking expertise in system design, multi-agent orchestration, and implementation.
Professionals Building Next-Generation AI Systems

Aiming to use advanced frameworks like GenAI, LangChain, and multi-agent systems to build reliable, scalable, real-world AI applications.

Program Curriculum

Designed by MIT faculty, the curriculum covers key concepts in Generative AI, Agentic AI, Data Science, and Machine Learning. Learn from experts through a structured, hands-on learning experience that builds the technical intuition and strategic judgment needed to translate data and AI into measurable business impact.

Pre-Work

Concepts Covered

- AI Landscape - Introduction To AI - Key AI Terminology And Workflows - Real-World AI Applications Across Industries - Evolution From Classical ML To GenAI And Autonomous Agents

Week 1: AI, GenAI, and Agentic AI Landscape

Concepts Covered

- AI Landscape - Introduction to AI - Key AI Terminology and Workflows - Real-World AI Applications Across Industries - Evolution from Classical ML to GenAI and Autonomous Agents

Week 2: LLMs and Prompt Engineering

Concepts Covered

- Foundations of Gen AI - Foundation Models and In-Context Learning - Prompt Engineering - LLM Training and Output Generation Mechanisms - Business Applications of LLMs - Prompt Engineering Techniques for Accuracy and Efficiency

Week 3: AI-Assisted Python Coding

Concepts Covered

- How AI Coding Assistants Generate Python Code - Accelerating Python Development With AI Tools - Debugging and Evaluating AI-Generated Code - Effective Prompt Design for Python Problem-Solving - Security and Efficiency Considerations in AI-Generated Code

Week 4: AI-Assisted Exploratory Analysis

Concepts Covered

- Data Exploration – Structured Data - Clustering Techniques: K-Means, K-Medoids, Gaussian Mixture Models - Dimensionality Reduction: PCA, t-SNE - Pattern Extraction From Structured Data - Visualization Of High-Dimensional Data

Week 5: Project 1

Work on a real-world challenge by applying skills learned throughout the program, leveraging industry-relevant tools and technologies to maximize outcomes.

Week 6: Predictive Modeling With Regression

Concepts Covered

- Prediction Methods – Regression - Fundamentals of Supervised Learning - Linear Regression for Numerical Prediction - Model Validation and Statistical Assumption Testing - Performance Metrics for Regression Models

Week 7: Building Decision Systems With AI

Concepts Covered

- Decision Systems - Classification With Decision Trees - Ensemble Methods: Random Forest - Classification Metrics and Model Evaluation - GenAI-Based Text Classification

Week 8: AI-Powered Recommendation Systems

Concepts Covered

- Recommendation Systems - Rank-Based Recommendations - Content-Based Filtering - Collaborative Filtering - Building Personalized Recommendation Engines With GenAI

Week 9: Project 2

Work on a real-world challenge by applying skills learned throughout the program, leveraging industry-relevant tools and technologies to maximize outcomes.

Week 10: Learning Break

Learning breaks are structured pauses to consolidate concepts, complete pending work, and reinforce understanding before progressing further.

Week 11: Building Context-Aware AI Workflows

Concepts Covered

- Transformers - Transformer Architecture and Self-Attention Mechanism - Multimodal Applications of Transformer Models - External Knowledge Sources for LLM Accuracy - Data Chunking and Embeddings for Retrieval - Building and Evaluating RAG Pipelines

Week 12: Prompt Optimization and Evaluation

Concepts Covered

- Evaluation of Gen AI Content - Text Evaluation Metrics: ROUGE and BERTScore - LLM-as-a-Judge Evaluation Methodology - Hallucination Detection Through Consistency Checks - Prompt Optimization for Model Reliability

Week 13: Project 3

Work on a real-world challenge by applying skills learned throughout the program, leveraging industry-relevant tools and technologies to maximize outcomes.

Week 14: Designing and Building Agentic AI Workflows

Concepts Covered

- Reinforcement Learning and Introduction to Agents - Reinforcement Learning Fundamentals: States, Actions, Rewards - Q-Learning and Policy Gradient Algorithms - Reactive LLMs vs. Autonomous AI Agents - Agent Architecture: Memory, Planning, Tool Use - Building Single-Agent Systems for Business Problems

Week 15: Orchestrating Multi-Agent Systems

Concepts Covered

- Multi-Agent Collaboration Frameworks - Dynamic Work Routing Across Agents - Adaptive RAG in Multi-Agent Workflows - Error Handling and Uncertainty Management - Performance Evaluation: Tool Accuracy and Handoff Reliability

Week 16: Project 4

Work on a real-world challenge by applying skills learned throughout the program, leveraging industry-relevant tools and technologies to maximize outcomes.

Self-Paced Modules

This module is designed to build practical capability in applying Generative AI and Agentic AI using the Claude ecosystem in real-world contexts. Learners build the ability to design, execute, and evaluate AI-driven workflows for real-world applications, supported by ~5 hours of structured learning.

- Design and Execute AI Workflows - Design and Execute AI Workflows

Build a foundational understanding of deep learning concepts and neural network architectures used in modern AI systems.

- Introduction to Deep Learning - Building Blocks of Neural Networks - Training Neural Networks - Digit Recognition

Learn how AI systems process and interpret visual information using advanced computer vision techniques.

- Drawbacks of ANN - Building Blocks of Convolutional Neural Networks - Training Convolutional Neural Networks - Image Detection

Explore the principles of building fair, transparent, and responsible AI systems across real-world applications.

- Introduction to AI Lifecycle - Introduction to Bias and Its Examples - Introduction to Causality and Privacy - Interconnections and Domains - Interdependency and Feedback in AI Systems

Understand the fundamentals of time-series data analysis and forecasting for temporal decision-making.

- Recognize why Time Series is a unique data modality that requires special techniques for analysis - Describe the components of a Time Series - Identification and estimation of Time Series components - Application of simple methods for Time Series Forecasting

Sample Case Studies

Apply your learning through real-world case studies guided by global industry experts. Please note: All case studies and projects outlined are indicative and subject to change.

Supply Chain Disruption Response Assistant

SUPPLY CHAIN Detect shipment delays and inventory shortfalls, analyze downstream order impact, and recommend rerouting actions to improve operational resilience. Tools and Concepts: Prompt Engineering, LLMs, AI Agents, Supply Chain Analytics, Decision Systems, Workflow Automation

Clinical Trial Protocol Feasibility Review

HEALTHCARE Analyze clinical trial protocols against regulatory requirements and generate structured go/no-go recommendation reports for new drug candidates. Tools and Concepts: Advanced Prompt Engineering, LLMs, Regulatory Analysis, Document Reasoning, Evaluation Frameworks, Decision Systems

AI-Assisted Data Cleaning for Retail Sales

RETAIL Build and debug Python-based data cleaning pipelines for retail sales data to ensure accurate and reliable inputs for downstream analysis and reporting. Tools and Concepts: AI Coding Assistants, Python, Data Cleaning, Prompt Design, Data Preprocessing, Debugging, Data Quality Assurance

Customer Segmentation for a Retail Bank

FINANCE Analyze transaction, demographic, and product data to identify customer segments for targeted cross-sell and retention strategies. Tools and Concepts: EDA, K-Means, Gaussian Mixture Models, PCA, Clustering, Feature Engineering, Data Visualization

Quick-Commerce Order Volume Drivers

RETAIL Identify key drivers of daily order volume to support decisions on store placement, promotions, and delivery service levels. Tools and Concepts: EDA, Linear Regression, Predictive Modeling, Feature Analysis, Statistical Modeling, Business Insights

Loan Application Risk Triage

FINANCE Classify loan applications into approve, manual review, or reject using structured financial data and free-text analysis to automate decisions and flag borderline cases. Tools and Concepts: Decision Trees, Random Forest, GenAI Text Classification, Classification Models, Feature Engineering, Model Evaluation, Risk Modeling

E-Commerce Next-Best-Product Recommendations

RETAIL Generate personalized product recommendations using browsing, cart, and purchase behavior to improve discovery and cross-sell performance. Tools and Concepts: Rank-Based Recommendations, Content-Based Filtering, Collaborative Filtering, Recommender Systems, User Behavior Modeling, Personalization Algorithms

Employee HR Policy Assistant

HR Retrieve and generate policy-compliant answers to employee queries on leave, reimbursements, and benefits using organizational knowledge sources. Tools and Concepts: Retrieval-Augmented Generation (RAG), Chunking, Embeddings, Vector Databases, Information Retrieval, LLM Grounding, Question Answering Systems

Banking Customer Service Copilot

FINANCE Evaluate and optimize a customer service assistant to deliver accurate, compliant, and consistent responses grounded in verified internal knowledge sources. Tools and Concepts: RAG, ROUGE, BERTScore, LLM-as-a-Judge, Prompt Optimization, Evaluation Metrics, Hallucination Detection, Response Validation, LLM Reliability

Autonomous SaaS Support Triage Agent

TECH Monitor support requests, classify intent, retrieve relevant customer context and resolutions, and draft contextual response recommendations for review. Tools and Concepts: AI Agents, Intent Classification, RAG, Email Processing, Context Retrieval, Workflow Automation, Response Generation

Competitive Market Intelligence Platform

RETAIL Collect and analyze competitor and market data using multiple agents, then consolidate insights into executive-ready reports. Tools and Concepts: Multi-Agent Systems, Adaptive RAG, Sentiment Analysis, Data Aggregation, Information Retrieval, Report Generation, Workflow Orchestration

Projects and Case Studies

Engage in projects and real-world case studies using emerging tools and technologies across sectors

AI-Assisted

Coding
10+

case studies
Advanced AI

Modules and Concepts

RETAIL

Customer Personality Segmentation

Description

Analyze customer purchase and demographic data to identify distinct behavioral segments that enable personalized loyalty programs and targeted promotions.

Skills you will learn

EDA
K-Means
Gaussian Mixture Models
PCA
Clustering
Dimensionality Reduction
Feature Engineering
Customer Analytics

MEDIA

OTT Platform Content Recommendation Engine

Description

Build a recommendation engine using viewing behavior and content metadata to improve discovery, increase watch time, and reduce churn.

Skills you will learn

Rank-Based Recommendations
Content-Based Filtering
Collaborative Filtering
Recommender Systems
User Behavior Analysis
Ranking Models
Personalization Algorithms

LEGAL

RAG-Based Legal Contract Review Assistant

Description

Build a contract review assistant that retrieves and analyzes clauses from internal contract repositories to support faster, more consistent legal review.

Skills you will learn

Retrieval-Augmented Generation (RAG)
Chunking
Embeddings
Vector Databases
Document Analysis
Information Retrieval
LLM Grounding

FINANCE

Multi-Agent Financial Research Assistant

Description

Build a multi-agent system that gathers, analyzes, and synthesizes financial data from filings, internal research, and external sources into structured sector briefings.

Skills you will learn

Multi-Agent Systems
Adaptive RAG
Web Search
Sentiment Analysis
Information Retrieval
Orchestration
Tool Use
Evaluation Metrics
Handoff Reliability

Languages and Tools covered

Build a solid foundation in popular/advanced tools and frameworks top employers seek

Python
Google Colab
VS Code (Visual Studio Code)
OpenAI
ChatGPT
LangChain
LangGraph
Claude

Earn a certificate of completion from MIT IDSS

Certificate from the MIT School of Engineering and IDSS upon successful completion of the program

World #1

MIT ranks #1 in World Universities – QS World University Rankings, 2025
U.S. #2

MIT ranks #2 among National Universities – U.S. News & World Report Rankings, 2024–2025

* Image for illustration only. Certificate subject to change.

Program Faculty

Stefanie Jegelka

Associate Professor, EECS and IDSS

Expert in algorithms and optimization for AI.

Pioneer advancing theoretical machine learning foundations.

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Munther Dahleh

William A. Coolidge Professor, EECS and IDSS; Founding Director, IDSS

Trailblazer in robust control and computational design.

Director propelling interdisciplinary research and innovation.

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Caroline Uhler

Professor, EECS and IDSS

Expert in computational biology, statistics, and systems.

Award-winning scholar relentlessly driving transformative data insights.

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Devavrat Shah

Andrew (1956) and Erna Viterbi Professor, EECS and IDSS

Renowned expert in large-scale network inference.

Award-winning innovator in data-driven decisions.

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John N. Tsitsiklis

Clarence J. Lebel Professor, Dept. of Electrical Engineering & Computer Science (EECS) at MIT

Leader in optimization, control, and learning.

Renowned scholar with multiple prestigious accolades.

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Program Mentors

Interact with dedicated and experienced industry experts who will guide you in your learning & career journey

Jagan Chidella

Information Technology Specialist III (Principal Enterprise Architect) at California Department of Technology.
Aishwarya Krishna Allada

Senior Data Scientist
Anuj Saboo

Business Intelligence & Data Science Manager at BAT.
Reza Bagheri

Data Scientist
Osama Sidahmed

Data Scientist III at Fortra.
Venugopal Adep

AI Product Leader - General Manager at Jio Platforms Limited (JPL).
Chetan Jangamashetti

Product Data Scientist
Sriharsha Ramaraju

Senior Data Scientist at Psychiatry-UK.
Mohammad Raahemi

Data Scientist at Canada Border Services Agency.
Abhishek Kumar Mishra

Manager - Data Science & ML Ops at Collinson.
Saurabh Kango

Manager Data and AI programs at Meta.

Learners review our program support & mentors

Read about learners experiences with our program support, faculty, and mentors

The program empowered me to confidently build and apply machine learning models in real-world scenarios.

With a background in Industrial Engineering and over two years of experience in manufacturing and finance, I recognized the need to elevate my technical toolkit. I was motivated to dive deep into building Machine Learning models and bring data-driven innovation to my organization. The program proved to be a game-changer, advancing my understanding of models and enabling me to confidently select and deploy algorithms. I now apply tools like Python and Machine Learning to projects impacting organizational strategy. Balancing coursework with a full-time role was challenging, but meticulous planning made it worthwhile. The program significantly boosted my professional confidence, equipping me to approach complex data challenges strategically. I now transform raw data into valuable insights, building a strong foundation for future growth.

Read More

Maximiliano Salinas De Leon

Financial & Data Analyst , Reacciones Químicas

19th Feb 2026
The program strengthened my ability to apply rigorous, data-driven thinking to business decisions.

I have over a decade of experience in data, analytics, and business intelligence, working in senior leadership. I wanted a more rigorous, academically grounded understanding of Data Science and Machine Learning, especially for data-driven business decisions. The MIT Data Science and Machine Learning: Making Data-Driven Decisions program stood out due to its strong theoretical foundation and real-world applicability. It deepened my understanding of statistical thinking, classification techniques, and Machine Learning fundamentals, using Python-based projects and business case studies. I applied these concepts to improve analytical rigor in revenue optimization and customer behavior analysis. Balancing the program with a full-time executive role was challenging, but the structured curriculum and clear explanations helped me stay disciplined. The program strengthened my confidence in advanced analytics discussions. I now approach problems with a methodical, hypothesis-driven mindset, considering data quality and ethical implications. This program has elevated how I frame business problems and communicate insights.

Read More

Asiedu Ansu

Head of Data and Analytics Centre of Excellence , Vodafone Ghana

6th Feb 2026
Only a well-structured program can genuinely facilitate successful learning in any subject.

I recently completed a Data Science and Machine Learning program with Great Learning, and it was an amazing experience! Although there are many 'free' or 'inexpensive' programs available online today, I found that only a well-structured program like this one can genuinely facilitate successful learning in any subject. From the beginning, the program stood out due to its comprehensive design aimed at ensuring success. It wasn't just about providing access to instructional videos; the program was crafted to support learners every step of the way. One of the most invaluable aspects was the mentoring sessions. These sessions provided a platform to discuss issues and resolve any doubts, which is essential no matter how good the videos are. Additionally, the program offered robust support whenever I encountered difficulties. In my case, having a dedicated Program Manager made a great difference. The combination of detailed instructional content, regular mentoring, and exceptional support ensured that I learned and truly understood and could apply the concepts of Data Science and Machine Learning.

Read More

Salvador Ruz

Founder , tecnifinkar (an startup)

19th Apr 2024
El curso sobrepasó mi expectativa, altamente recomendado.

Soy el CIO de la Universidad Pedagógica Nacional. En la Universidad Pedagógica Nacional tengo el deseo de apoyar las investigaciones de las áreas de posgrado en materia de Ciencia de Datos, además con esto realizar colaboraciones interinstitucionales a nivel internacional sobre el tema. Me pareció un curso excelente, sobrepasó mi expectativa, altamente recomendado. Si bien es cierto la exigencia de repente rebasó las cargas de trabajo, al final fue muy satisfactorio el aprendizaje, los contenidos muy pertinentes, el nivel de profundidad adecuado y el material es muy bueno, sobre todo que podremos seguir accediendo a él durante un tiempo, lo cual da oportunidad de profundizar en algunos temas y recordar algunos otros. Los aprendizajes clave, fueron el retomar los conceptos aprendidos de probabilidad y estadística, aprendiendo también nuevos conceptos y su aplicación. Y sobre todo revisar el potencial de Python, que en mi caso, en programación, aunque había tenido experiencia, lo había dejado desde hace muchos años por dedicarme solo a la gestión de TI.

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Victor Alvarez

subdirector , universidad pedagógica nacional

28th Sep 2023
La adopción de Ciencia de Datos es un requerimiento inmediato en mi institución.

"Mi interés por la Ciencia de Datos es porque en las instituciones donde colaboro la adopción de estas tecnologías es ya un requerimiento inmediato, tanto en la parte jurisdiccional como en la parte estadística. Mi experiencia la considero muy positiva, considero diseñar un proyecto para llevar lo aprendido en la institución donde trabajo y para proyectos personales. Aprendí Python y sus principales librerías, la interpretación de los resultados."

Read More

Otilio Hernandez

titular de la unidad de servicios informaticos (cio) , tribunal electoral de la ciudad de méxico

26th Sep 2023
El contenido bien estructurado y material de alta calidad transformaron mi capacidad en la Supply Chain.

"Mi principal motivación fue fortalecer mi capacidad de toma de decisiones basadas en datos y combinar técnicas avanzadas de análisis con mi experiencia en Supply Chain. Buscaba herramientas y habilidades que me permitieran generar soluciones innovadoras y eficientes en el mundo de la gestión de la cadena de suministro. El contenido estaba bien estructurado, y el material de aprendizaje fue de alta calidad. Los casos de estudio presentados por la facultad del MIT eran relevantes y actuales. Los proyectos me permitieron aplicar el conocimiento teórico en escenarios prácticos. Además, el apoyo de la oficina del programa fue esencial para mi proceso de aprendizaje. Lo más interesante fue cómo la ciencia de datos y el aprendizaje automático pueden transformar y optimizar los sistemas de cadena de suministro, llevándolos a un nuevo nivel de eficiencia y precisión. Las habilidades clave que adquirí incluyen técnicas avanzadas de análisis de datos, modelado predictivo y toma de decisiones basadas en datos. Aprendí cómo aplicar estos métodos en escenarios reales, lo que ha ampliado significativamente mi capacidad de aportar valor en roles relacionados con la cadena de suministro. Recomendaría que se sumerjan completamente en el programa, aprovechando todos los recursos y oportunidades de aprendizaje que ofrece."

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Eugenio Araya

subdirector de gestión y desarrollo , pontificia universidad católica de chile

25th Sep 2023
Challenging hands-on projects were worthwhile and transformed my expectations for data teams.

I am 55 years old and decided to tackle this detailed program in Machine Learning and Data Analysis, given the promise of hands-on experience. The program structure and content are highly relevant and have certainly changed what I expect from my data teams going forward. Remote learning through the Olympus platform worked very well. The content was excellent, with all lectures transcribed for future reference. The hands-on projects were challenging but worthwhile. The mentor learning sessions on weekends were detailed and an essential part of the program. I found the program support to be excellent, and it was reassuring to have someone just a WhatsApp message away for help. I have already recommended this program to others.

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Kobus Burger

head of data&analytics for client solutions , standard bank group

17th Jul 2023
Highly recommended for anyone seeking a career in Data Science.

The program is well-structured and brilliantly taught. The live sessions by industry experts were incredible. The projects and case studies provided practical experience. The Program Manager's support started before the program and continues even after completion. A highly recommended program for anyone seeking a career in Data Science.

Read More

Aseem Mehrotra

instrumentation maintenance & reliability engineer , adnoc group

15th Jul 2023
The weekend live sessions were really helpful for clearing doubts with an expert.

The teaching methodology was really nice. The program provided me with a lot of practical quizzes and wonderful notebooks. The weekend live sessions were really helpful as I could directly talk to an expert and ask all my doubts and queries.

Read More

Fabrizio De Masi

fieldbus & iot engineer , gefran

19th Jun 2023
A well-structured, comprehensive, and challenging learning journey over 12 weeks.

The MIT Data Science and Machine Learning program with Great Learning was an enriching experience over 12 weeks. The program was well-structured, comprehensive, and challenging, making it an impactful learning journey. The commitment to excellence from the MIT faculty, coupled with the support of the learning team and mentors, created a highly supportive environment. The Program Manager was pivotal in ensuring smooth communication and guidance, contributing significantly to the program's success. I highly recommend this program to those seeking a thorough understanding of Data Science and Machine Learning.\

Read More

Anca Fatu

power system specialist , independent electricity system operator

19th Jun 2023

Learners review

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"The people behind the program were amazing, I believe this was best part of the program"

The favourite part was the hackathon competition, where we had to combine everything that we had learnt and build the model

Arlindo Almada

,
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" Mentors help you understand difficult concepts and complete the course"

Studying this course has placed me in a better position to offer good counseling in my field. I am going to stretch myself to work as a Data Scientist in the business industry. I see this opportunity as a dream come true.

Berthy Buah

STMIE Coordinator , Ghana Education Service
Watch story

"Building Confidence in Big Data Management Without Prior Experience"

Joined the program to learn handling big data and exceeded expectations. Gained valuable skills in Python and Machine Learning. Highly recommend it for anyone starting their data analytics journey!

Chun Wing Ip

Student , University Of Sydney

Course Fees

The course fee is USD 2,500

Invest in your career

Learn from world-renowned MIT IDSS faculty and top industry leaders
Build an impressive portfolio with 3 projects and 50+ case studies
Get personalized assistance with a dedicated Program Manager from Great Learning
Earn a certificate of completion from MIT IDSS and 8.0 Continuing Education Units (CEUs)

Take the next step

00 : 00 : 00

Apply to the program now or schedule a call with a program advisor

Unlock exclusive course sneak peek

Application Closes: 14th May 2026

Application Process

The program follows a simple 3-step application process. The step-by-step process is outlined below.

1. Fill application form

Apply by filling a simple online application form.
2. Application Screening

A panel from Great Learning will review your application to determine your fit for the program your fit for the program.
3. Join program

After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Limited seats available

Learn AI, Data Science, and Machine Learning with MIT Faculty

Batch start date

Online · To be announced

Admissions Open

Apply Now

Delivered in Collaboration with:

MIT Institute for Data, Systems, and Society (IDSS) is collaborating with online education provider Great Learning to offer AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact. This program leverages MIT's leadership in innovation, science, engineering, and technical disciplines developed over years of research, teaching, and practice. Great Learning collaborates with institutions to manage enrollments (including all payment services and invoicing), technology, and participant support. Accessibility

Got more questions? Talk to us

Connect with our advisors and get your queries resolved

Speak with our expert +1 617 539 7216 or email to ai-ds.mit.idss@mygreatlearning.com

Frequently asked questions

Program Details

Eligibility and Registration

Fee Related Queries

Others

Program Details

What does the MIT IDSS AI and Data Science course offer?

The 12-week online AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact is offered by the MIT Institute for Data, Systems, and Society (IDSS). The program offers:

A certificate of completion from MIT IDSS and the MIT Schwarzman College of Computing
Mentorship from experienced industry experts
Recorded lectures by MIT faculty.
Exposure to cutting-edge topics, including Generative AI, Responsible AI, Deep Learning, and more
A comprehensive curriculum covering both foundational and advanced concepts.
Flexibility and practical value that working professionals need.

What makes the MIT IDSS AI and Data Science program unique?

MIT IDSS AI and Data Science program is unique because of its academic rigor and industry relevance. Here are the reasons why this program stands out:

Learn from MIT Faculty

Access recorded lectures from 13 world-renowned MIT faculty and instructors who bring academic depth and industry relevance to every session.

Get Mentored by Industry Experts

Receive direct mentorship from professionals working in the world’s leading organizations as they share real-world applications of Data Science and AI concepts.

Real-World Expertise

Work on 3 hands-on projects and explore over 50 real-world case studies to strengthen your skills and demonstrate your AI and Data Science capabilities.

Explore the Future of AI

Attend 3 exclusive masterclasses on Generative AI to understand the latest developments and how they are shaping industries worldwide.

Master Key AI Concepts

Deepen your understanding of core AI concepts, including Generative AI, Recommendation Systems, Responsible AI, and Deep Learning.

Earn a Recognized Credential

Receive a Certificate of Completion from MIT IDSS and earn 8.0 Continuing Education Units (CEUs), validating your ability to apply AI and Data Science for impact.

Career Support

Benefit from dedicated career support, including tailored CV and LinkedIn profile reviews designed to support your transition or advancement in the field.

What are the learning outcomes of the MIT IDSS AI and Data Science course?

With this AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact, learners will gain:

Understand the intricacies of Data Science and Artificial Intelligence techniques and their applications to real-world problems
Implement various Machine Learning techniques to solve complex problems and make data-driven business decisions
Explore two major realms of Artificial Intelligence: Machine Learning and Deep Learning, and understand how they apply to domains such as Computer Vision and Recommendation Systems
Choose how to represent your data effectively when making predictions
Explore the practical applications of Recommendation Systems across various industries and business contexts
Build an industry-ready portfolio of projects and demonstrate your ability to extract valuable business insights from data

What is the required weekly time commitment for this AI and Data Science program?

The program is designed for professionals and typically requires 8–12 hours per week. This includes:

Around 2 hours of recorded faculty lectures
2 hours of weekend hands-on mentorship sessions (held over 7 weekends)
Additional time for self-study, assignments, and project work

The format ensures you can balance learning with your professional responsibilities.

How is my performance evaluated in the MIT IDSS AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact?

Your progress is measured through continuous assessments designed to reinforce learning and real-world application.

These include:

Quizzes and graded assignments
Case studies and hands-on exercises
Capstone project to test applied understanding

This approach ensures you stay engaged. It tracks your learning outcomes throughout the 12-week program.

Do I need to bring my own laptop for this AI and Data Science online course?

Yes, having your own laptop is required for attending the program. The necessary technology requirements shall be shared during registration.

Who is the faculty of this AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact?

This program is taught by renowned MIT faculty with over 120 years of collective experience in academic leadership, research, and industry collaboration. They have published award-winning papers and made significant contributions to AI, Data Science, and Machine Learning. In addition to the faculty, the course also features global industry mentors who will guide you through live, personalized mentoring sessions as you work on hands-on projects.

What languages and tools will I learn in this AI and Data science course?

You will learn the most in-demand languages and tools during the AI and Data Science Program, including:

Python
Pandas
NumPy
Keras
TensorFlow
Matplotlib
Scikit-Learn and others.

How is the curriculum of this AI and Data Science course unique?

The curriculum, expertly crafted by MIT faculty, is designed to equip learners with industry-relevant tools and techniques, enabling them to apply these skills in AI, Data Science, Machine Learning, and Generative AI to real-world problems. Here's what makes it unique:

Crafted by MIT faculty for academic depth and industry relevance.
Covers complete AI and Data Science concepts from foundational techniques to advanced machine learning models, deep learning, NLP, computer vision, and recommendation systems.
Focus on Generative AI and Responsible AI to ensure you're equipped for the next wave of innovation.
Provides Hands-On Experience through 3 industry-relevant projects and over 50 real-world case studies.
Built for Working Professionals with a flexible format, recorded lectures, and live weekend mentorship.

What role does Great Learning play in delivering this program?

Yes, this program is delivered by MIT IDSS in collaboration with Great Learning. As an education collaborator, Great Learning supports learners throughout their journey by providing access to experienced industry mentors, program support teams, and live personalized mentorship sessions.

Great Learning also facilitates learner engagement, offers career guidance, and ensures a seamless learning experience aligned with the high academic standards set by MIT IDSS.

Is this Data Science course online?

Yes. The program is delivered 100% online to meet the needs of working professionals. You can build practical skills in AI, Data Science, and Machine Learning from anywhere, all within a focused and efficient 12-week format.

Will I receive a certificate after completing the MIT IDSS AI and Data Science course for working professionals?

Upon successful completion of the AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact, you will receive a Certificate of Completion from MIT IDSS and earn 8.0 Continuing Education Units (CEUs), validating your ability to apply AI and Data Science for impact.

How is MIT ranked globally?

MIT is ranked #1 university in the world (QS World University Rankings) in AI and Data Science and the #2 national university in the U.S. (U.S. News & World Report). MIT Professional Education's programs reflect this leadership by integrating cutting-edge concepts and practical skill-building in these domains, enabling professionals to make data-driven decisions and lead digital transformation.

Will I receive a transcript or grade sheet after completing this AI and Data Science course?

As this is not a degree program or a full-time academic offering, the university does not issue transcripts or grade sheets for this course.

However, you will receive performance scores for each assessment and module. Upon successfully completing all program requirements, you will earn a Certificate of Completion from the MIT Schwarzman College of Computing and IDSS.

What is the duration of this MIT IDSS AI and Data Science Program?

The duration of the MIT IDSS AI and Data Science Program is 12 weeks. It includes recorded lectures from award-winning MIT faculty, more than 50 real-world case studies, and 3 industry-relevant hands-on projects.

Eligibility and Registration

Who is this program ideal for?

The AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact is ideal for:

Early-career professionals or senior managers (IT Managers, Business Intelligence Analysts, Data Science Managers, Management Consultants, and Business Managers) who want to apply AI, Data Science, and Machine Learning techniques in their firms.

Data Scientists, Data Analysts, or Business Analysts who wish to turn vast volumes of data into valuable insights

Entrepreneurs interested in innovation with the assistance of AI, Data Science, and Machine Learning techniques

Those with academic or professional training in Applied Statistics or Mathematics will find the program easier to learn. However, participants without such a background can also complete the program, provided they are ready to put in extra effort. Great Learning will offer the required assistance.

What is the deadline to enroll in this AI and Data Science course from MIT IDSS?

The application process is conducted on a rolling basis and will close once the required number of candidates are enrolled. Apply early to secure your seat. To ensure your chances of securing a seat, we encourage you to apply as early as possible.

What coding skills are helpful for an AI and Data Professional?

A successful AI and Data Professional needs a firm grasp of coding skills. Here are the key skills that help a professional learn Data Science:

Python: Python is the most widely used language in the field of data analysis and Machine Learning.

Foundational Knowledge of Other Languages: Familiarity with R, SQL, and SAS is essential, especially for statistical analysis and database management. Some roles may also require exposure to Java, Scala, or Julia.

Understanding of the Data Workflow: Beyond programming, you’ll need to know how to work with tools for data acquisition, cleaning, transformation, warehousing, and visualization.

What is the registration process to pursue this online MIT IDSS AI and Data Science Program?

The registration process for this AI and Data Science program is as follows:

Step-1: Application Form Register by completing the online application form.
Step-2: Fill Application Screening A panel from Great Learning will assess your application based on academics, work experience, and motivation.
Step-3: Join Program After a final review, you will receive an offer for a seat in the upcoming cohort of the program.

Fee Related Queries

What is the program fee?

The total program fee is USD 2500.

Do I need to pay any additional charges for buying books, virtual learning material, or license fees?

No. All required learning materials are provided online through the Learning Management System (LMS). Because these fields continue to evolve, you will also receive a list of recommended books and resources for optional, in-depth exploration.

What are the available payment options for registering for the online Data Science course from MIT IDSS?

Applicants can pay the program fee through Bank Transfer and Credit/Debit Cards. They can also pay in easy installments using PayPal credit options and get interest-free payments for up to 6 months (Note that these services are subject to credit approval by PayPal). [For further details, please get in touch with us at dsml.mit@mygreatlearning.com]

Is there any refund policy?

Please note that submitting the registration fee constitutes enrollment in the program, and the cancellation penalties outlined below will be applied. If you are unable to attend your program, please review our dropout and refund policies below:

Dropout requests received within 7 days of enrollment and more than 42 days prior to the commencement of the program will incur no fee. Any payment received will be refunded in full.

Dropout requests received more than 42 days prior to the program but more than 7 days after the acceptance are subject to a cancellation fee of USD 250.

Dropout requests received 22-41 days prior to the commencement of the program are subject to a cancellation fee equal to 50% of the program fee.

Any dropout requests received fewer than 22 days prior to the commencement of the program are subject to a cancellation fee equal to 100% of the program fee.

No refund will be made to those who do not engage in the program or leave before completing a program for which they have registered.

Are there any corporate sponsorship programs?

We accept corporate sponsorships and can assist you with the process. [For more information, please write to us at [dsml.mit@mygreatlearning.com]

Others

Is there a demand for AI, Data Science, and Machine Learning specialists?

Yes, there is an increasing demand for AI, Data Science, and Machine Learning professionals across a wide range of industries, including Technology, Healthcare, Cybersecurity, Finance, Oil & Gas, Transportation, Education, Talent Acquisition, Inventory Management, E-commerce, and more.

As organizations increasingly adopt AI and data-driven decision-making, professionals skilled in AI, Data Science, Machine Learning, and Generative AI are in high demand. These skills offer strong opportunities for career growth, leadership roles, and long-term industry relevance.

Employment of data scientists is projected to grow 34 percent from 2024 to 2034, significantly faster than the average growth rate for all occupations. If you’re looking to build a future-proof career, now is the time to upskill in AI, Data Science, and Machine Learning.

How to become a Data Scientist?

To become a Data Scientist, you need to have a blend of technical expertise, analytical thinking, and real-world problem-solving skills. If you have a strong academic background, that will help you learn the concepts related to the field.

Here’s how you can start:

Build a strong foundation in mathematics, statistics, and programming.
Gain hands-on experience with tools used in the industry.
Develop applied knowledge through projects that simulate real-world data challenges.
Strengthen your understanding of Generative AI, AI, and Machine Learning techniques.

Is the future of AI and data science promising?

Yes, the future of AI and Data Science is highly promising. As organizations become more AI and data-driven, the demand for professionals who can turn raw data into strategic insights is growing. Here’s why:

Growing demand in Business: Companies are leveraging AI and Data Science to reduce costs, improve marketing effectiveness, launch better products, and tap into new markets.

Data-driven strategy making: Gartner has forecasted that many corporate strategies will highlight data and analytics as essential business competencies.

Expanding applications: From healthcare and finance to retail and tech, Data Science is shaping decision-making and driving innovation across industries.

Career longevity: With data at the core of digital transformation, professionals with expertise in AI, ML, and Data Science are well-positioned for long-term career growth.

As industries increasingly rely on data to drive innovation and growth, the need for skilled Data Science professionals will only continue to rise.

Gaining expertise in Artificial Intelligence, Machine Learning, and Data Science is a smart investment for you to future-proof your career.

What is Machine Learning?

Machine Learning refers to a group of techniques used by data scientists that allow computers to learn from data. From leisure to work, our lives are made easier with Machine Learning. The responsibilities of a Machine Learning specialist encompass a spectrum that extends from creating Machine Learning models to retraining systems.

What is Data Science?

Data Science is a field of study that uses a scientific approach to extract meaningful insights from data. Meaningful insights are derived from data sets, generating knowledge that advises recommendations for business growth.

Why choose Data Science and Machine Learning?

Organizations across industries increasingly rely on advanced Data Science and Machine Learning to drive strategic decision-making and improve business outcomes. Here’s how Data Science and Machine Learning create impact.

Inform stronger business strategies.

Leading companies use data-driven insights and machine learning models to design effective business plans, optimize operations, and accelerate growth.

Deliver solutions that meet customer needs.

Organizations with clear data strategies can anticipate market trends, innovate faster, and build products that offer greater value to end users.

Reduce operational costs.

For small and medium-sized enterprises, AI, Data Science, and Machine Learning enable more efficient processes and cost-effective solutions, helping them stay competitive despite limited resources.

This combination of strategic insight, customer-centric innovation, and operational efficiency is why Data Science and Machine Learning have become essential capabilities for modern businesses.

What is the average salary of an AI, Data Science, or Machine Learning Professional?

The average salary of an AI professional is $184,757. For a Data Scientist, it is $129,294 and for a Machine Learning Specialist, it is $186,067.

Design Intelligent Systems using LLMs, RAG, and Agentic AI

AI and Data Science: Leveraging Responsible AI, Data and Statistics for Practical Impact

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Top rated online AI, Data Science, and ML course

Distinctive features

AI-assisted coding

Next-gen modules

Elevate your career in AI, Data Science and ML

Earn a certificate of completion from MIT IDSS

Why choose the AI and Data Science program

Learn from MIT faculty

AI-assisted coding

Advanced AI-infused curriculum

Personalized mentorship sessions

Dedicated program support

Build Real-World Expertise

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

Agentic AI

Prompt Engineering

Retrieval-Augmented Generation (RAG)

Multi-Agent Systems

LLM Orchestration

Prompt Optimization

AI-Assisted Coding

LLM Evaluation

AI Workflow Design

Generative AI Applications

This program is ideal for

Program Curriculum

Pre-Work

Concepts Covered

Week 1: AI, GenAI, and Agentic AI Landscape

Concepts Covered

Week 2: LLMs and Prompt Engineering

Concepts Covered

Week 3: AI-Assisted Python Coding

Concepts Covered

Week 4: AI-Assisted Exploratory Analysis

Concepts Covered

Week 5: Project 1

Week 6: Predictive Modeling With Regression

Concepts Covered

Week 7: Building Decision Systems With AI

Concepts Covered

Week 8: AI-Powered Recommendation Systems

Concepts Covered

Week 9: Project 2

Week 10: Learning Break

Week 11: Building Context-Aware AI Workflows

Concepts Covered

Week 12: Prompt Optimization and Evaluation

Concepts Covered

Week 13: Project 3

Week 14: Designing and Building Agentic AI Workflows

Concepts Covered

Week 15: Orchestrating Multi-Agent Systems

Concepts Covered

Week 16: Project 4

Self-Paced Modules

Build a foundational understanding of deep learning concepts and neural network architectures used in modern AI systems.

Learn how AI systems process and interpret visual information using advanced computer vision techniques.

Explore the principles of building fair, transparent, and responsible AI systems across real-world applications.

Understand the fundamentals of time-series data analysis and forecasting for temporal decision-making.

Sample Case Studies

Supply Chain Disruption Response Assistant

Clinical Trial Protocol Feasibility Review

AI-Assisted Data Cleaning for Retail Sales

Customer Segmentation for a Retail Bank

Quick-Commerce Order Volume Drivers

Loan Application Risk Triage